Smart-device-based radar system detecting user gestures in the presence of saturation

ABSTRACT

Techniques and apparatuses are described that implement a smart-device-based radar system capable of detecting user gestures in the presence of saturation. In particular, a radar system 104 employs machine learning to compensate for distortions resulting from saturation. This enables gesture recognition to be performed while the radar system 104&#39;s receiver 304 is saturated. As such, the radar system 104 can forgo integrating an automatic gain control circuit to prevent the receiver 304 from becoming saturated. Furthermore, the radar system 104 can operate with higher gains to increasing sensitivity without adding additional antennas. By using machine learning, the radar system 104&#39;s dynamic range increases, which enables the radar system 104 to detect a variety of different types of gestures having small or large radar cross sections, and performed at various distances from the radar system 104.

BACKGROUND

Radar-based gesture recognition can enable a user to interact with asmall-screen smart device, such as a smartphone or a smart watch,without using virtual keyboards or screen-based gestures. In many cases,a radar sensor may replace a bulky and expensive sensor, such as acamera, and provide improved gesture-recognition performance in thepresence of different environmental conditions, such as low lighting andfog, or with moving or overlapping targets. While it may be advantageousto use the radar sensor, there are many challenges associated withintegrating the radar sensor in commercial devices and using the radarsensor for gesture recognition.

One such problem involves restrictions that a small consumer device mayplace on a radar sensor's design. To satisfy size or layout constraints,for example, hardware circuitry within the radar sensor may be downsizedby reducing a quantity of antenna elements or foregoing certain hardwarecomponents, such as an automatic gain control circuit. Consequently, adynamic range of the radar sensor can be reduced, which limits the radarsensor's ability to perform gesture recognition. With fewer antennaelements, for example, the radar may be unable to recognize gestures atfarther distances. Also, without an automatic gain control circuit,close range gestures can saturate the radar sensor's receiver and resultin signal clipping. While the receiver is saturated, a noise floor ofthe receiver can increase, thereby decreasing measurement accuracies,increasing a false alarm rate, and decreasing sensitivity performance ofthe radar sensor.

SUMMARY

Techniques and apparatuses are described that implement asmart-device-based radar system capable of detecting user gestures inthe presence of saturation. In particular, a radar system includes asaturation compensation module that employs machine learning to improvethe radar system's dynamic range. More specifically, the saturationcompensation module accepts a saturated version of a radar receivesignal and generates a non-saturated version of the radar receivesignal. With this non-saturated version, the radar system can accuratelydetermine range, Doppler frequency, angle, and radar cross section of anobject for gesture recognition. As such, the radar system can detectgestures performed by a user at close ranges that saturate the radarsystem's receiver.

By using machine learning to compensate for distortions caused by thesaturated receiver, the radar system can avoid additional hardwarecomplexity and cost associated with integrating an automatic gaincontrol circuit to prevent the receiver from becoming saturated.Furthermore, the machine learning can be trained to recover motioncomponent signals that are associated with a variety of differentgestures and are affected by different amounts of signal clipping. Tofurther improve the dynamic range, the radar system can operate withhigher gains to increase sensitivity, despite the increased likelihoodof the receiver becoming saturated. In this way, the radar system'ssensitivity increases without increasing a quantity of antennas. Withimproved dynamic range, the radar system can detect a variety ofdifferent types of gestures having small or large radar cross sections,and being performed at various distances from the radar system.

Aspects described below include an apparatus with a radar system. Theradar system includes at least one antenna, a transceiver, a saturationcompensation module, and a gesture recognition module. The transceiveris coupled to the at least one antenna and is configured to transmit,via the at least one antenna, a radar transmit signal. The transceiveris also configured to receive, via the at least one antenna, a radarreceive signal, which includes a portion of the radar transmit signalthat is reflected by a user. The transceiver is further configured togenerate, based on the radar receive signal, a saturated radar receivesignal with a clipped amplitude. The saturation compensation module iscoupled to the transceiver and is configured to generate, based on thesaturated radar receive signal and using machine learning, a predictedsignal, which comprises a sinusoidal signal. The gesture recognitionmodule is coupled to the saturation compensation module and isconfigured to determine a gesture performed by the user based on thepredicted signal.

Aspects described below also include a method for performing operationsof a smart-device-based radar system capable of detecting user gesturesin the presence of saturation. The method includes transmitting a radartransmit signal and receiving a radar receive signal. The radar receivesignal includes a portion of the radar transmit signal that is reflectedby a user. The method also includes generating, based on the radarreceive signal, a saturated radar receive signal with a clippedamplitude. The method further includes generating, based on thesaturated radar receive signal and using a machine-learned module, apredicted signal, which comprises a sinusoidal signal. The method alsoincludes determining a gesture performed by the user based on thepredicted signal.

Aspects described below also include a computer-readable storage mediacomprising computer-executable instructions that, responsive toexecution by a processor, implement a saturation compensation module anda gesture recognition module. The saturation compensation module isconfigured to accept an input data sequence associated with a saturatedradar receive signal. The saturated radar receive signal has a clippedamplitude that distorts a motion component signal associated with agesture performed by a user. The saturated radar receive signal includesa distorted version of the motion component signal. The saturationcompensation module is also configured to recover, using machinelearning, the motion component signal from the input data sequence toproduce a predicted data sequence based on the motion component signal.The predicted data sequence comprises a sinusoidal signal. The gesturerecognition module is configured to determine the gesture based on thepredicted data sequence.

Aspects described below also include a system with machine-learningmeans for recovering a motion component signal from a saturated radarreceive signal that includes a distorted version of the motion componentsignal.

BRIEF DESCRIPTION OF THE DRAWINGS

Apparatuses for and techniques implementing a smart-device-based radarsystem capable of detecting user gestures in the presence of saturationare described with reference to the following drawings. The same numbersare used throughout the drawings to reference like features andcomponents:

FIG. 1 illustrates example environments in which a smart-device-basedradar system capable of detecting user gestures in the presence ofsaturation can be implemented.

FIG. 2 illustrates an example implementation of a radar system as partof a smart device.

FIG. 3 illustrates an example operation of a radar system for detectinguser gestures in the presence of saturation.

FIG. 4 illustrates an example scheme performed by a saturationcompensation module for detecting user gestures in the presence ofsaturation.

FIG. 5 illustrates an example implementation of a machine-learned modulefor detecting user gestures in the presence of saturation.

FIG. 6 illustrates an example method for performing operations of asmart-device-based radar system capable of detecting user gestures inthe presence of saturation.

FIG. 7 illustrates an example computing system embodying, or in whichtechniques may be implemented that enable use of, a radar system capableof detecting user gestures in the presence of saturation.

DETAILED DESCRIPTION

Overview

While it may be advantageous to use a radar sensor to detect usergestures, there are many challenges associated with integrating theradar sensor in commercial devices and using the radar sensor forgesture recognition. One such problem involves restrictions that a smallconsumer device may place on a radar sensor's design. To satisfy size orlayout constraints, for example, hardware circuitry within the radarsensor may be downsized by reducing a quantity of antenna elements orforegoing certain hardware components, such as an automatic gain controlcircuit. Consequently, a dynamic range of the radar sensor can bereduced, which limits the radar sensor's ability to perform gesturerecognition. With fewer antenna elements, for example, the radar may beunable to recognize gestures at farther distances. Also, without anautomatic gain control circuit, close range gestures can saturate theradar sensor's receiver and result in signal clipping. While thereceiver is saturated, a noise floor of the receiver can increase,thereby decreasing measurement accuracies, increasing a false alarmrate, and decreasing sensitivity performance of the radar sensor.

To address this problem, some techniques avoid saturating the radarsystem by limiting gesture recognition performance to distances that aresufficiently far from the radar system. This may make it cumbersome andawkward for the user to perform the gestures and interact with theconsumer device, however. Other techniques avoid saturating the radarsystem by implementing an automatic gain control circuit, whichautomatically reduces transmission power to prevent the radar systemfrom becoming saturated. Integrating the automatic gain control circuitwithin the radar system, however, can increase hardware complexity andcost of the radar system Furthermore, the automatic gain control circuitcan increase a footprint of the radar system, thereby making itimpractical for the radar system to be integrated within mobile devicesthat place a premium on small size and low weight.

In contrast, techniques described herein present a smart-device-basedradar system capable of detecting user gestures in the presence ofsaturation. In particular, a radar system includes a saturationcompensation module that employs machine learning to improve the radarsystem's dynamic range. More specifically, the saturation compensationmodule accepts a saturated version of a radar receive signal andgenerates a non-saturated version of the radar receive signal. With thisnon-saturated version, the radar system can accurately determine range,Doppler frequency, angle, and radar cross section of an object forgesture recognition. As such, the radar system can detect gesturesperformed by a user at close ranges that saturate the radar system'sreceiver.

By using machine learning to compensate for distortions caused by thesaturated receiver, the radar system can avoid additional hardwarecomplexity and cost associated with integrating an automatic gaincontrol circuit to prevent the receiver from becoming saturated.Furthermore, the machine learning can be trained to recover motioncomponent signals that are associated with a variety of differentgestures and are affected by different amounts of signal clipping. Tofurther improve the dynamic range, the radar system can operate withhigher gains to increase sensitivity, despite the increased likelihoodof the receiver becoming saturated. In this way, the radar system'ssensitivity increases without increasing a quantity of antennas. Withimproved dynamic range, the radar system can detect a variety ofdifferent types of gestures having small or large radar cross sections,and being performed at various distances from the radar system.

Example Environment

FIG. 1 is an illustration of example environments in which techniquesusing, and an apparatus including, a smart-device-based radar systemcapable of detecting user gestures in the presence of saturation may beembodied. In the depicted environments 100-1, 100-2, and 100-3, a smartdevice 102 includes a radar system 104 capable of performing gesturerecognition. Although the smart device 102 is shown to be a smartphonein FIG. 1 , the smart device 102 can be implemented as any suitablecomputing or electronic device, as described in further detail withrespect to FIG. 2 .

In the environments 100-1 to 100-3, a user performs different types ofgestures, which are detected by the radar system 104. For example, theuser in environment 100-1 makes a scrolling gesture by moving a handabove the smart device 102 along a horizontal dimension (e.g., from aleft side of the smart device 102 to a right side of the smart device102). In the environment 100-2, the user makes a reaching gesture, whichdecreases a distance between the smart device 102 and the user's hand.The users in environment 100-3 make hand gestures to play a game on thesmart device 102. In one instance, a user makes a pushing gesture bymoving a hand above the smart device 102 along a vertical dimension(e.g., from a bottom side of the smart device 102 to a top side of thesmart device 102).

The radar system 104 can also recognize other types of gestures ormotions not shown in FIG. 1 . Example types of gestures include, aknob-turning gesture in which a user curls their fingers to grip animaginary door knob and rotate their fingers and hand in a clockwise orcounter-clockwise fashion to mimic an action of turning the imaginarydoor knob. Another example type of gesture includes a spindle-twistinggesture, which a user performs by rubbing a thumb and at least one otherfinger together. The gestures can be two-dimensional, such as thoseusable with touch-sensitive displays (e.g., a two-finger pinch, atwo-finger spread, or a tap). The gestures can also bethree-dimensional, such as many sign-language gestures, e.g., those ofAmerican Sign Language (ASL) and other sign languages worldwide. Upondetecting each of these gestures, the smart device 102 may perform anaction, such as display new content, move a cursor, activate one or moresensors, open an application, and so forth. In this way, the radarsystem 104 provides touch-free control of the smart device 102.

In some situations, at least a portion of a gesture performed by theuser is at a sufficiently far distance from the radar system 104 or hasa sufficiently small radar cross section such that radar system 104 isnot saturated. In this case, the radar system 104 generates anon-saturated signal 106, as shown in a graph 108 at the bottom right ofFIG. 1 . As the radar system 104 is not saturated by the gesture, signalclipping does not occur and the non-saturated signal 106 is a sinusoidalsignal having a non-clipped amplitude. Characteristics of thenon-saturated signal 106 can therefore be directly analyzed by the radarsystems 104 for gesture recognition.

In other situations, however, at least a portion of a gesture is at asufficiently close distance to the radar system 104 or has asufficiently large radar cross section such that the radar system 104 issaturated. Without an automatic gain control circuit to automaticallyadjust transmission power to avoid the saturation, signal clippingoccurs and the radar system 104 generates a saturated signal 110, asshown in a graph 112 at the bottom left of FIG. 1 . Due to the signalclipping, the saturated signal 110 is a non-sinusoidal signal. Morespecifically, the signal clipping causes an amplitude of the saturatedsignal 110 to be constrained within a saturation threshold 114 of theradar system 104. In other words, at least a portion of the amplitude ofthe saturated signal 110 is relatively constant and does not linearlyincrease based on an amplitude of a reflected radar signal. Thisclipping makes it challenging for the radar system 104 to recognize thegestures performed by the user directly from the saturated signal 110.Using machine learning, however, the radar system 104 can be trained torecover a sinusoidal signal from the saturated signal 110 to improvegesture recognition as well as other radar functions, such as presencedetection, human vital-sign detection, collision avoidance, and soforth.

Some implementations of the radar system 104 are particularlyadvantageous as applied in the context of smart devices 102, for whichthere is a convergence of issues such as a need for limitations in aspacing and layout of the radar system 104, low power, and other issues.Although the implementations are particularly advantageous in thedescribed context of a system for which gesture recognition is required,it is to be appreciated that the applicability of the features andadvantages of the present invention is not necessarily so limited, andother implementations involving other types of electronic devices mayalso be within the scope of the present teachings.

Exemplary overall lateral dimensions of the smart device 102 can be, forexample, approximately eight centimeters by approximately fifteencentimeters. Exemplary footprints of the radar system 104 can be evenmore limited, such as approximately four millimeters by six millimeterswith antennas included. Exemplary power consumption of the radar system104 may be on the order of a few milliwatts to several milliwatts (e.g.,between approximately two milliwatts and twenty milliwatts). Therequirement of such a limited footprint and power consumption for theradar system 104, enables the smart device 102 to include otherdesirable features in such a space-limited package (e.g., a camerasensor, a fingerprint sensor, a display, and so forth). The smart device102 and the radar system 104 are further described with respect to FIG.2 .

FIG. 2 illustrates the radar system 104 as part of the smart device 102.The smart device 102 can be any suitable computing device or electronicdevice, such as a desktop computer 102-1, a tablet 102-2, a laptop102-3, a smartphone 102-4, a smart speaker 102-5, a security camera102-6, a smart thermostat 102-7, a microwave 102-8, and a vehicle 102-9.Other devices may also be used, such as home-service devices, babymonitors, routers, computing watches, computing glasses, gaming systems,televisions, drones, track pads, drawing pads, netbooks, e-readers,home-automation and control systems, and other home appliances. Thesmart device 102 can be wearable, non-wearable but mobile, or relativelyimmobile (e.g., desktops and appliances). The radar system 104 can beused as a stand-alone radar system or used with, or embedded within,many different computing devices or peripherals, such as in controlpanels that control home appliances and systems, in automobiles tocontrol internal functions (e.g., volume, cruise control, or evendriving of the car), or as an attachment to a laptop computer to controlcomputing applications on the laptop.

The smart device 102 includes one or more computer processors 202 andcomputer-readable media 204, which includes memory media and storagemedia. Applications and/or an operating system (not shown) embodied ascomputer-readable instructions on the computer-readable media 204 can beexecuted by the computer processor 202 to provide some of thefunctionalities described herein. The computer-readable media 204 alsoincludes a radar-based application 206, which uses radar data generatedby the radar system 104 to perform a function, such as gesture-basedcontrol, presence detection, human vital-sign notification, or collisionavoidance for autonomous driving.

The smart device 102 also includes a network interface 208 forcommunicating data over wired, wireless, or optical networks. Forexample, the network interface 208 communicates data over alocal-area-network (LAN), a wireless local-area-network (WLAN), apersonal-area-network (PAN), a wire-area-network (WAN), an intranet, theInternet, a peer-to-peer network, a point-to-point network, a meshnetwork, and the like. The smart device 102 may also include a displayor speakers (not shown).

The radar system 104 includes a communication interface 210 to transmitthe radar data to a remote device, though this need not be used if theradar system 104 is integrated within the smart device 102. In general,the radar data provided by the communication interface 210 is in aformat usable by the radar-based application 206.

The radar system 104 also includes at least one antenna 212 and at leastone transceiver 214 to transmit and receive radar signals. The antenna212 can be circularly polarized, horizontally polarized, or verticallypolarized. In some cases, the radar system 104 includes multipleantennas 212 implemented as antenna elements of an antenna array. Theantenna array can include at least one transmitting antenna element andat least two receiving antenna elements. In some situations, the antennaarray includes multiple transmitting antenna elements to implement amultiple-input multiple-output (MIMO) radar capable of transmittingmultiple distinct waveforms at a given time (e.g., a different waveformper transmitting antenna element). The receiving antenna elements can bepositioned in a one-dimensional shape (e.g., a line) or atwo-dimensional shape (e.g., a triangle, a rectangle, or an L-shape) forimplementations that include three or more receiving antenna elements.The one-dimensional shape enables the radar system 104 to measure oneangular dimension (e.g., an azimuth or an elevation) while thetwo-dimensional shape enables two angular dimensions to be measured(e.g., both azimuth and elevation).

Using the antenna array, the radar system 104 can form beams that aresteered or un-steered, wide or narrow, or shaped (e.g., as a hemisphere,cube, fan, cone, or cylinder). The one or more transmitting antennaelements may have an un-steered omnidirectional radiation pattern or maybe able to produce a wide steerable beam. Either of these techniquesenable the radar system 104 to illuminate a large volume of space. Toachieve target angular accuracies and angular resolutions, the receivingantenna elements can be used to generate thousands of narrow steeredbeams (e.g., 2000 beams, 4000 beams, or 6000 beams) with digitalbeamforming. In this way, the radar system 104 can efficiently monitoran external environment and detect gestures from one or more users.

The transceiver 214 includes circuitry and logic for transmitting andreceiving radar signals via the antenna 212. Components of thetransceiver 214 can include amplifiers, mixers, switches,analog-to-digital converters, filters, and so forth for conditioning theradar signals. The transceiver 214 also includes logic to performin-phase/quadrature (I/Q) operations, such as modulation ordemodulation. A variety of modulations can be used to produce the radarsignals, including linear frequency modulations, triangular frequencymodulations, stepped frequency modulations, or phase modulations. Thetransceiver 214 can be configured to support continuous-wave or pulsedradar operations.

A frequency spectrum (e.g., range of frequencies) that the transceiver214 can use to generate radar signals can encompass frequencies between1 and 400 gigahertz (GHz), between 1 and 24 GHz, between 2 and 6 GHz,between 4 and 100 GHz, or between 57 and 63 GHz. In some cases, thefrequency spectrum can be divided into multiple sub-spectrums that havesimilar or different bandwidths. Example bandwidths can be on the orderof 500 megahertz (MHz), one gigahertz (GHz), two gigahertz, and soforth. Different frequency sub-spectrums may include, for example,frequencies between approximately 57 and 59 GHz, 59 and 61 GHz, or 61and 63 GHz. Although the example frequency sub-spectrums described aboveare contiguous, other frequency sub-spectrums may not be contiguous. Toachieve coherence, multiple frequency sub-spectrums (contiguous or not)that have a same bandwidth may be used by the transceiver 214 togenerate multiple radar signals, which are transmitted simultaneously orseparated in time. In some situations, multiple contiguous frequencysub-spectrums may be used to transmit a single radar signal, therebyenabling the radar signal to have a wide bandwidth.

The radar system 104 may also include one or more system processors 216and a system media 218 (e.g., one or more computer-readable storagemedia). Although the system processor 216 is shown to be separate fromthe transceiver 214 in FIG. 2 , the system processor 216 may beimplemented within the transceiver 214 as a digital signal processor ora low-power processor, for instance. The system processor 216 executescomputer-readable instructions that are stored within the system media218. Example digital operations performed by the system processor 216include Fast-Fourier Transforms (FFTs), filtering, modulations ordemodulations, digital signal generation, digital beamforming, and soforth.

The system media 218 includes a saturation compensation module 220 and agesture recognition module 222 (e.g., a human gesture recognition module222). The saturation compensation module 220 employs machine learning torecover a sinusoidal signal from a saturated non-sinusoidal signal. Inother words, the saturation compensation module 220 analyzes a saturatedversion of a reflected radar signal and generates a non-saturatedversion of the reflected radar signal that does not include thedistortions resulting from the saturation. Using the saturationcompensation module 220, the radar system 104 can perform gesturerecognition while saturated and realize increased dynamic range. If theradar system 104 is not saturated, the saturation compensation module220 can also process non-saturated signals 106 without degradingperformance of the radar system 104.

The saturation compensation module 220 relies on supervised learning andcan use simulated (e.g., synthetic) data or measured (e.g., real) datafor machine-learning training purposes, as further described withrespect to FIG. 4 . Training enables the saturation compensation module220 to learn a non-linear mapping function for translating a saturatedversion of a radar receive signal into a predicted signal thatrepresents a non-saturated version of the radar receive signal.

The saturation compensation module 220 can include one or moreartificial neural networks (referred to herein as neural networks). Aneural network includes a group of connected nodes (e.g., neurons orperceptrons), which are organized into one or more layers. As anexample, the saturation compensation module 220 includes a deep neuralnetwork, which includes an input layer, an output layer, and one or morehidden layers positioned between the input layer and the output layers.The nodes of the deep neural network can be partially-connected or fullyconnected between the layers.

In some cases, the deep neural network is a recurrent deep neuralnetwork (e.g., a long short-term memory (LSTM) recurrent deep neuralnetwork) with connections between nodes forming a cycle to retaininformation from a previous portion of an input data sequence for asubsequent portion of the input data sequence. In other cases, the deepneural network is a feed-forward deep neural network in which theconnections between the nodes do not form a cycle. Additionally oralternatively, the saturation compensation module 220 can includeanother type of neural network, such as a convolutional neural network.An example deep neural network is further described with respect to FIG.6 . The saturation compensation module 220 can also include one or moretypes of regression models, such as a single linear regression model,multiple linear regression models, logistic regression models, step-wiseregression models, multi-variate adaptive regression splines, locallyestimated scatterplot smoothing models, and so forth.

Generally, a machine learning architecture of the saturationcompensation module 220 can be tailored based on available power,available memory, or computational capability. The machine learningarchitecture can also be tailored based on a quantity of gestures theradar system 104 is designed to recognize. In some cases, the saturationcompensation module 220 can be trained to automatically recoverinformation associated with a variety of different types of gestures. Inthis way, the radar system 104 can seamlessly provide gesturerecognition as a user performs different gestures that may or may notsaturate the radar system 104.

Alternatively, to reduce a complexity of the saturation compensationmodule 220, the saturation compensation module 220 can be re-trained fordifferent sets of gestures performed by the user. In this case, theradar-based application 206 can prompt the user to select a set ofgestures or automatically determine the set of gestures based on arunning application or gesture-based controls that are currentlyavailable to the user. The radar-based application 206 informs thesaturation compensation 220 of the selected set of gestures, whichenables the saturation compensation module 220 to initiate a trainingprocedure for the set of gestures.

The gesture recognition module 222 receives the predicted signal fromthe saturation compensation module 220 and analyzes the predicted signalto determine the gesture performed by the user. In some cases, thegesture recognition module 222 uses the communication interface 210 toinform the radar-based application 206 of the determined gesture.

Although shown to be included within the system media 218, otherimplementations of the saturation compensation module 220 and/or thegesture recognition module 222 can be included, at least partially,within the computer-readable media 204. In this case, at least somefunctionality of the saturation compensation module 220 or the gesturerecognition module 222 can be by the computer processor 202. Althoughnot shown, the system media 218 can also include other types of modules,such as a user detection module, a human vital-sign detection module, acollision avoidance module, a digital beamforming module, and so forth.The radar system 104 is further described with respect to FIG. 3 .

Detecting User Gestures in the Presence of Saturation

FIG. 3 illustrates an example operation of the radar system 104 fordetecting user gestures in the presence of saturation. In the depictedconfiguration, the radar system 104 is shown to include the antenna 212,the transceiver 214, and the system processor 216. The antenna 212 isindirectly or directly coupled to the transceiver 214, which includes atransmitter 302 and a receiver 304. The system processor 216 is coupledto the transceiver 214 and executes the saturation compensation module220 and the gesture recognition module 222.

The receiver 304 includes components, such as a pre-amplifier, alow-noise amplifier, a variable gain amplifier, or a baseband amplifier,which have a limited dynamic range. If these components are subject tosignals with amplitudes that exceed a corresponding saturationthreshold, the components can clip the signals and produce distortedsignals. Due to signal clipping, a signal-to-noise ratio of the signaldecreases as the signal's power is constrained and this constraintincreases the power associated with noise. The increase in the noisepower further raises a noise floor of the receiver 304, which can makeit challenging to detect weaker signals associated with other users orother gestures.

During operation, the transmitter 302 generates and provides a radartransmit signal 306 to the antenna 212. As an example, the radartransmit signal 306 is a continuous-wave frequency-modulated signal, asillustrated in FIG. 3 . The antenna 212 transmits the radar transmitsignal 306, which impinges on a user. Consequently, a radar receivesignal 308 is reflected from the user and includes at least a portion ofthe radar transmit signal 306. Due to the Doppler effect, however, afrequency of the radar receive signal 308 differs from the radartransmit signal 306 based on a motion of the user. More specifically,the radar receive signal 308 includes a motion component signal 310,which includes amplitude and frequency information associated with themotion of the user.

The receiver 304 receives the radar receive signal 308 via the antenna212 and processes the radar receive signal 308 (e.g., amplifies,downconverts, filters, demodulates, or digitizes the radar receivesignal 308). In particular, the receiver 304 mixes a version of theradar receive signal 308 with a version of the radar transmit signal 306to generate a beat signal. A frequency of the beat signal represents afrequency offset between the radar transmit signal 306 and the radarreceive signal 308. This frequency varies based on the motion of theuser. In this manner, the beat signal includes the motion componentsignal 310.

In situations in which an amplitude of the radar receive signal 308causes the receiver 304 to become saturated, however, the receiver 304generates a saturated beat signal, which is referred to herein as asaturated radar receive signal 312. A clipped amplitude of the saturatedradar receive signal 312 distorts the motion component signal 310, andresults in the saturated radar receive signal 312 including a distortedmotion component signal 314 (e.g., a distorted version of the motioncomponent signal 310). The saturated radar receive signal 312 includes atemporal sequence of samples, which are provided as an input datasequence to the saturation compensation module 220, as shown in FIG. 4 .

The saturation compensation module 220 generates a predicted signal 316based on the saturated radar receive signal 312. In particular, thesaturation compensation module 220 processes different sets of samplesbased on a temporal processing window, recovers the motion componentsignal 310 from within these sets of samples, and outputs sets ofpredicted samples that are associated with the motion component signal310. In effect, the saturation compensation module 220 compensates fordistortions within the saturated radar receive signal 312 to produce thepredicted signal 316 based on the motion component signal 310. As such,the predicted signal 316 has a larger signal-to-noise ratio relative tothe saturated radar receive signal 312. By processing the predictedsignal 316 for gesture recognition, the radar system 104 can realizeimproved measurement accuracies, a decreased false alarm rate, andimproved sensitivity. Although not explicitly shown, the receiver 304 orthe system processor 216 can also include a band-pass filter thatfilters the radar receive signal 308 for frequencies outside a generalfrequency range of the motion component signal 310 prior to providingthe saturated radar receive signal 312 to the saturation compensationmodule 220.

The gesture recognition module 222 determines a gesture performed by theuser based on the predicted signal 316. More specifically, the gesturerecognition module 222 analyzes the predicted signal 316 to measure aposition (e.g., range, azimuth, and/or elevation) or range rate of oneor more scattering points associated with the user. With thesemeasurements, the gesture recognition module 222 can determine othertypes of information to further assist with gesture recognition,including velocity (e.g., speed), acceleration, or radar cross section.The gesture recognition module 222 can also use FFTs, digitalbeamforming, or prediction and tracking algorithms to determine thisinformation. The scatting points can be associated with differentregions on the user's appendage or different appendages, such asdifferent fingers or hands, different portions of the user's hand,different portions of the user's arm, and so forth. Depending on thetype of gesture performed, these scattering points may move differentlywith respect to each other. By analyzing the information associated withthe scattering points over time, the gesture recognition module 222determines the gesture. Operations performed by the saturationcompensation module 220 are further described with respect to FIG. 4 .

FIG. 4 illustrates an example scheme performed by the saturationcompensation module 220 for detecting user gestures in the presence ofsaturation. In the depicted configuration, the saturation compensationmodule 220 includes a training module 402, a normalization module 404,and a machine-learned module 406. In general, the machine-learned module406 can be implemented using one or more of the machine learningarchitectures described above with respect to FIG. 2 . An exampleimplementation of the machine-learned module 406 is further describedwith respect to FIG. 5 .

The training module 402 is coupled to the normalization module 404 andthe machine-learned module 406. The normalization module 404 is alsocoupled to an input of the saturation compensation module 220, which canbe coupled to the receiver 304 (of FIG. 3 ). The machine-learned model406 is coupled to the normalization module 404 and an output of thesaturation compensation module 220, which can be coupled to the gesturerecognition module 222 (of FIG. 3 ).

The training module 402 provides a training data sequence 408 and truthdata 410 for training the machine-learned module 406 to recover themotion component signal 310 from the saturated radar receive signal 312.The training data sequence 408 and the truth data 410 can be based onsimulated data or measured data, either of which can be stored withinthe system media 218 or generated in real time during an initializationprocedure. Although the training module 402 is shown to be includedwithin the saturation compensation module 220 in FIG. 4 , the trainingmodule 402 can alternatively be implemented separate from the saturationcompensation module 220.

In the simulated data case, the training module 402 generates sinusoidalsignals to simulate non-saturated radar receive signals that representprobable motion component signals. The sinusoidal signals can beperiodic signals and vary in frequency from each other. In some cases,the sinusoidal signals represent different types of gestures performedby the user, such as those described above with respect to FIG. 1 . Thetruth data 410 includes the sinusoidal signals, which the trainingmodule 402 provides to the machine-learned module 406 during a trainingprocedure.

Additionally, the training module 402 generates non-sinusoidal signalshaving different clipped amplitudes to simulate probable saturated radarreceive signals. The non-sinusoidal signals are associated withdifferent amplitudes of a radar receive signal. As such, an amount ofclipping observed within the non-sinusoidal signals varies across thenon-sinusoidal signals. Furthermore, frequencies of the non-sinusoidalsignals correspond to the frequencies of the sinusoidal signals. Ingeneral, the sinusoidal signals and the non-sinusoidal signals aregenerated to have a similar quantity of samples. The non-sinusoidalsignals can also be periodic. These non-sinusoidal signals form thetraining data sequence 408, which the training module 402 provides tothe normalization module 404, as shown in FIG. 4 , or to themachine-learned module 406 if the training data sequence 408 isnormalized.

In the measured data case, the training module 402 can be coupled to aproximity sensor within the smart device 102, which measures distancesto the user. The proximity sensor can be a camera, an infra-red sensor,and so forth. The training module 402 receives the measurement data fromthe proximity sensor and generates the truth data 410 based on themeasurement data. In particular, the training module 402 generatessinusoidal signals that have different frequencies that represent thedifferent measured distances to the user. Additionally, the trainingmodule 402 is coupled to the transceiver 214 of FIG. 3 , and causes theradar system 104 to operate (e.g., transmit one or more radar transmitsignals 306 and receive one or more radar receive signals 308) during atime period that the proximity sensor measures the distances to theuser. In this way, a gesture performed by the user during this timeperiod is captured by the radar receive signals 308. The training module402 can perform an extrapolation operation to generate the training datasequence 408 based on the radar receive signal 308. The training module402 provides the truth data 410 to the machine-learned module 406 andthe training data sequence 408 to the normalization module 404 duringthe training procedure. The training procedure is further describedbelow.

The normalization module 404 performs a normalization operation thatgenerates a normalized data sequence 412 based on an input signal (e.g.,an input data sequence 414 or the training data sequence 408). As oneexample, the normalization module 404 normalizes the input signal bysubtracting a mean value of the input signal across a given dimension'sfeature values from each individual feature value and then dividing bythe standard deviation or another metric. By normalizing the inputsignal, the saturation compensation module 220 is able to account foramplitude variations resulting from changes in a user's distance fromthe radar system 104 during gesture recognition. This normalizationoperation also enables the machine-learned module 406 to efficientlydetermine machine-learning parameters (e.g., weights and biasparameters) that optimize a cost function (e.g., an objective function).

During a training procedure, the training module 402 provides a trainingdata sequence 408 to the normalization module 404 and associated truthdata 410 to the machine-learned module 406. The normalization module 404normalizes the training data sequence 408 and provides a normalized datasequence 412 to the machine-learned module 406. The machine-learnedmodule 406 processes the normalized data sequence 412 and generates apredicted data sequence 418. The machine-learned module 406 alsodetermines the machine-learning parameters that minimize an errorbetween the resulting predicted data sequence 418 and the truth data 410using a cost function, such as a mean square error. As an example, themachine-learned module 406 uses a gradient descent method to optimizethe cost function. Generally speaking, this training procedure enablesthe machine-learned module 406 to effectively recover the motioncomponent signal 310 from the saturated radar receive signal 312 andgenerate the predicted data sequence 418 based on the motion componentsignal 310.

During gesture recognition, the normalization module 404 accepts theinput data sequence 414 from an input of the saturation compensationmodule 220. As described with respect to FIG. 3 , this input datasequence 414 can represent the saturated radar receive signal 312, whichis provided by the receiver 304. The normalization module 404 normalizesthe saturated radar receive signal 312 and provides the normalized datasequence 412 to the machine-learned module 406. Using themachine-learning parameters determined during the training procedure,the machine-learned module recovers the motion component signal 310 fromthe normalized data sequence 412 and generates the predicted datasequence 418 based on the motion component signal 310. Themachine-learned module 406 is further described with respect to FIG. 5 .

FIG. 5 illustrates an example implementation of the machine-learnedmodule 406 for determining user gestures in the presence of saturation.In the depicted configuration, the machine-learned module 406 isimplemented as a deep neural network and includes an input layer 502,multiple hidden layers 504, and an output layer 506. The input layer 502includes multiple inputs 508-1, 508-2 . . . 508-N, where N represents apositive integer equal to a quantity of samples corresponding to thetemporal processing window. The multiple hidden layers 504 includelayers 504-1, 504-2 . . . 504-M, where M represents a positive integer.Each hidden layer 504 includes multiple neurons, such as neurons 510-1,510-2 . . . 510-Q, where Q represents a positive integer. Each neuron510 is connected to at least one other neuron 510 in a previous hiddenlayer 504 or a next hidden layer 504. A quantity of neurons 510 can besimilar or different between different hidden layers 504. In some cases,a hidden layer 504 can be a replica of a previous layer (e.g., layer504-2 can be a replica of layer 504-1). The output layer 506 includesoutputs 512-1, 512-2 . . . 512-N.

Generally speaking, a variety of different deep neural networks can beimplemented with various quantities of inputs 508, hidden layers 504,neurons 510, and outputs 512. A quantity of layers within themachine-learned module 406 can be based on the quantity of gestures andthe complexity of the motion component signals 310 the saturationcompensation module 220 is designed to recover. As an example, themachine-learned module 406 includes four layers (e.g., one input layer502, one output layer 506, and two hidden layers 504) to recover themotion component signal 310 associated with a reaching gesture (e.g.,such as in the example environment 100-2 of FIG. 1 ). Alternatively, thequantity of hidden layers can be on the order of a hundred to enable thesaturation compensation module 220 to recover motion component signalsassociated with a fine or complex gesture, such as a knob-turninggesture, a spindle twisting gesture, or a three-dimensional gesture.

During gesture recognition, a set of input samples associated with thenormalized data sequence 412 is provided to the input layer 502 based onthe temporal processing window. Assuming the saturated radar receivesignal 312 is generated based on a sampling rate of 20 Hz and a size ofthe temporal processing window represents a duration of 4 seconds, theset of input samples includes 80 samples, and a quantity of inputs 508and outputs 512 (e.g., N) is equal to 80. Each neuron 510 in the hiddenlayers 504 analyzes a different section or portion of the set of inputsamples for different features. As an example, a first hidden layer504-1 includes 10 neurons and a second hidden layer 504-2 includes eightneurons. Together, the hidden layers 504 compensate for disturbancesthat are present within the saturated radar receive signal 312 torecover the motion component signal 310. At the output layer 506, a setof predicted samples is generated, which is based on the motioncomponent signal 310. The gesture recognition module 222 analyzes theset of predicted samples to recognize at least a portion of the gestureperformed during this time period.

The above operations can continue for a subsequent set of input sampleswithin the normalized data sequence 412. With training, themachine-learned module 406 can learn to recover a variety of differenttypes of motion component signals 310 for a variety of differentsaturation levels to enable gesture detection to be performed while theradar system 104 is saturated.

Example Methods

FIG. 6 depicts an example method 600 for performing operations of asmart-device-based radar system capable of detecting user gestures inthe presence of saturation. Method 600 is shown as sets of operations(or acts) performed but not necessarily limited to the order orcombinations in which the operations are shown herein. Further, any ofone or more of the operations may be repeated, combined, reorganized, orlinked to provide a wide array of additional and/or alternate methods.In portions of the following discussion, reference may be made to theenvironment 100-1 to 100-3 of FIG. 1 , and entities detailed in FIG. 2or 4 , reference to which is made for example only. The techniques arenot limited to performance by one entity or multiple entities operatingon one device.

At 602, a radar transmit signal is transmitted. For example, the radarsystem 104 transmits the radar transmit signal 306 using the transmitter302 and the antenna 212, as shown in FIG. 3 . The radar transmit signal306 can be a continuous-wave frequency-modulated signal (e.g., a chirpsignal).

At 604, a radar receive signal is received. The radar receive signalincludes a portion of the radar transmit signal that is reflected by auser. For example, the radar system 104 receives the radar receivesignal 308 using the receiver 304 and the antenna 212, as shown in FIG.3 . The radar receive signal 308 includes a portion of the radartransmit signal 306 that is reflected by the user, such as a user shownin the example environments 100-1 to 100-3 of FIG. 1 . The radar receivesignal 308 also includes a motion component signal 310, which isassociated with a gesture performed by the user. In some situations, adistance between the user and the radar system 104 or a radar crosssection of the gesture results in an amplitude of the radar receivesignal 308 saturating the receiver 304.

At 606, a saturated radar receive signal with a clipped amplitude isgenerated based on the radar receive signal. For example, the receiver304 generates the saturated radar receive signal 312 with the clippedamplitude shown in the graph 112 of FIG. 1 . The receiver 304 generatesthe saturated radar receive signal 312 based on the radar receive signal308. The receiver 304 can generate the saturated radar receive signal312 by amplifying, filtering, downconverting, demodulating, and/ordigitizing the radar receive signal 308. The saturated radar receivesignal 312 can include a distorted motion component signal 314 (of FIG.3 ), which represents a distorted version of the motion component signal310 due to signal clipping.

At 608, a predicted signal comprising a sinusoidal signal is generatedbased on the saturated radar receive signal and using a machine-learnedmodule. For example, the saturation compensation module 220 uses machinelearning to generate the predicted signal 316 based on the saturatedradar receive signal 312. The predicted signal 316 comprises asinusoidal signal, which represents the recovered motion componentsignal 310. In this way, the saturation compensation module 220compensates for distortions caused by the saturation and increasesperformance of the radar system 104 by generating the predicted signal316 to have a larger signal-to-noise ratio than the saturated radarreceive signal 312.

At 610, a gesture performed by the user is determined based on thepredicted signal. For example, the gesture recognition module 222determines a gesture performed by the user based on the predicted signal316.

Example Computing System

FIG. 7 illustrates various components of an example computing system 700that can be implemented as any type of client, server, and/or computingdevice as described with reference to the previous FIG. 2 to implementgesture recognition in the presence of saturation.

The computing system 700 includes communication devices 702 that enablewired and/or wireless communication of device data 704 (e.g., receiveddata, data that is being received, data scheduled for broadcast, or datapackets of the data). The device data 704 or other device content caninclude configuration settings of the device, media content stored onthe device, and/or information associated with a user of the device.Media content stored on the computing system 700 can include any type ofaudio, video, and/or image data. The computing system 700 includes oneor more data inputs 706 via which any type of data, media content,and/or inputs can be received, such as human utterances, user-selectableinputs (explicit or implicit), messages, music, television mediacontent, recorded video content, and any other type of audio, video,and/or image data received from any content and/or data source.

The computing system 700 also includes communication interfaces 708,which can be implemented as any one or more of a serial and/or parallelinterface, a wireless interface, any type of network interface, a modem,and as any other type of communication interface. The communicationinterfaces 708 provide a connection and/or communication links betweenthe computing system 700 and a communication network by which otherelectronic, computing, and communication devices communicate data withthe computing system 700.

The computing system 700 includes one or more processors 710 (e.g., anyof microprocessors, controllers, and the like), which process variouscomputer-executable instructions to control the operation of thecomputing system 700 and to enable techniques for, or in which can beembodied, gesture recognition in the presence of saturation.Alternatively or in addition, the computing system 700 can beimplemented with any one or combination of hardware, firmware, or fixedlogic circuitry that is implemented in connection with processing andcontrol circuits which are generally identified at 712. Although notshown, the computing system 700 can include a system bus or datatransfer system that couples the various components within the device. Asystem bus can include any one or combination of different busstructures, such as a memory bus or memory controller, a peripheral bus,a universal serial bus, and/or a processor or local bus that utilizesany of a variety of bus architectures.

The computing system 700 also includes a computer-readable media 714,such as one or more memory devices that enable persistent and/ornon-transitory data storage (i.e., in contrast to mere signaltransmission), examples of which include random access memory (RAM),non-volatile memory (e.g., any one or more of a read-only memory (ROM),flash memory, EPROM, EEPROM, etc.), and a disk storage device. The diskstorage device may be implemented as any type of magnetic or opticalstorage device, such as a hard disk drive, a recordable and/orrewriteable compact disc (CD), any type of a digital versatile disc(DVD), and the like. The computing system 700 can also include a massstorage media device (storage media) 716.

The computer-readable media 714 provides data storage mechanisms tostore the device data 704, as well as various device applications 718and any other types of information and/or data related to operationalaspects of the computing system 700. For example, an operating system720 can be maintained as a computer application with thecomputer-readable media 714 and executed on the processors 710. Thedevice applications 718 may include a device manager, such as any formof a control application, software application, signal-processing andcontrol module, code that is native to a particular device, a hardwareabstraction layer for a particular device, and so on.

The device applications 718 also include any system components, engines,or managers to implement gesture recognition in the presence ofsaturation. In this example, the device applications 718 include thesaturation compensation module 220 and the gesture recognition module222.

CONCLUSION

Although techniques using, and apparatuses including asmart-device-based radar system detecting user gestures in the presenceof saturation have been described in language specific to featuresand/or methods, it is to be understood that the subject of the appendedclaims is not necessarily limited to the specific features or methodsdescribed. Rather, the specific features and methods are disclosed asexample implementations of smart-device-based radar system detectinguser gestures in the presence of saturation.

The invention claimed is:
 1. A smart device comprising: a radar system,the radar system including: at least one antenna; a transceiver coupledto the at least one antenna and configured to: transmit, via the atleast one antenna, a radar transmit signal; receive, via the at leastone antenna, a radar receive signal, the radar receive signal includinga portion of the radar transmit signal that is reflected by a user, theradar receive signal causing the transceiver to become saturated; andresponsive to the transceiver becoming saturated, generate, based on theradar receive signal, a saturated radar receive signal with a clippedamplitude, the saturated radar receive signal comprising an analogsignal having an amplitude that is constrained by an upper bound or alower bound, the upper bound or the lower bound causing the amplitude ofthe analog signal to be relatively constant at the upper bound or thelower bound for a portion of the analog signal; a saturationcompensation module coupled to the transceiver and configured togenerate, based on the saturated radar receive signal and using machinelearning, a predicted signal, the predicted signal comprising asinusoidal signal; and a gesture recognition module coupled to thesaturation compensation module and configured to determine a gestureperformed by the user based on the predicted signal.
 2. The smart deviceof claim 1, wherein: the radar receive signal includes a motioncomponent signal associated with at least a portion of the gestureperformed by the user; the saturated radar receive signal includes adistorted version of the motion component signal based on the clippedamplitude; and the saturation compensation module is configured torecover the motion component signal from the saturated radar receivesignal such that the predicted signal is based on the motion componentsignal.
 3. The smart device of claim 1, wherein the transceiver isconfigured to generate the saturated radar receive signal responsive toan amplitude of the radar receive signal exceeding a saturationthreshold of the transceiver.
 4. The smart device of claim 1, whereinthe saturation compensation module includes a normalization modulecoupled to the transceiver, the normalization module configured tonormalize the saturated radar receive signal to produce a normalizeddata sequence, the normalized data sequence used to generate thepredicted signal.
 5. The smart device of claim 4, wherein the saturationcompensation module includes a machine-learned module coupled to thenormalization module, the machine-learned module configured to: accept aset of normalized samples within the normalized data sequence based on atemporal processing window, the normalized data sequence including amotion component signal associated with the gesture, the motioncomponent signal distorted within the normalized data sequence based onthe clipped amplitude of the saturated radar receive signal; and recoverthe motion component signal from the saturated radar receive signal toproduce a set of predicted samples associated with the predicted signal,the set of predicted samples based on the motion component signal, theset of normalized samples and the set of predicted samples havingsimilar quantities of samples based on a size of the temporal processingwindow.
 6. The smart device of claim 5, wherein: the saturationcompensation module includes a training module coupled to the machinelearned module and the normalization module, the training moduleconfigured to: provide a training data sequence to the normalizationmodule; and provide truth data to the machine-learned module; thenormalization module is configured to generate another normalized datasequence based on the training data sequence; and the machine-learnedmodule is configured to execute a training procedure to determinemachine-learning parameters based on the other normalized data sequenceand the truth data.
 7. The smart device of claim 6, wherein the trainingmodule is configured to: generate sinusoidal signals to simulatenon-saturated radar receive signals, the sinusoidal signals representingthe truth data; and generate non-sinusoidal signals having differentclipped amplitudes to simulate probable saturated radar receive signals,the non-sinusoidal signals representing saturated versions of thenon-saturated radar receive signals, the non-sinusoidal signalsrepresenting the training data sequence.
 8. The smart device of claim 7,wherein: the sinusoidal signals have different frequencies; andfrequencies of the non-sinusoidal signals correspond to the differentfrequencies of the sinusoidal signals.
 9. The smart device of claim 7,wherein the sinusoidal signals and the non-sinusoidal signals areperiodic.
 10. The smart device of claim 6, further comprising: aproximity sensor configured to measure different distances between thesmart device and the user, wherein the training module is coupled to theproximity sensor and configured to: generate the truth data based on themeasured distances; cause the radar system to transmit at least oneother radar transmit signal and receive at least one other radar receivesignal while the proximity sensor measures the distances; and generatethe training data sequence based on the at least one other radar receivesignal.
 11. The smart device of claim 1, wherein the saturationcompensation module includes a machine-learned module comprising a deepneural network with at least two hidden layers.
 12. A method comprising:transmitting a radar transmit signal using a transceiver of a radarsystem; receiving a radar receive signal using the transceiver, theradar receive signal including a portion of the radar transmit signalthat is reflected by a user, the radar receive signal causing thetransceiver to become saturated; responsive to the transceiver becomingsaturated, generating, based on the radar receive signal, a saturatedradar receive signal with a clipped amplitude, the saturated radarreceive signal comprising an analog signal having an amplitude that isconstrained by an upper bound or a lower bound, the upper bound or thelower bound causing the amplitude of the analog signal to be relativelyconstant at the upper bound or the lower bound for a portion of theanalog signal; generating, based on the saturated radar receive signaland using a machine-learned module, a predicted signal, the predictedsignal comprising a sinusoidal signal; and determining a gestureperformed by the user based on the predicted signal.
 13. The method ofclaim 12, wherein: the radar receive signal includes a motion componentsignal associated with the gesture; the generating of the saturatedradar receive signal causes the motion component signal within thesaturated radar receive signal to become distorted based on the clippedamplitude; and the generating of the predicted signal comprisesrecovering the motion component signal from the saturated radar receivesignal.
 14. The method of claim 13, wherein: the receiving of the radarreceive signal comprises receiving the radar receive signal using atransceiver; and the causing of the motion component signal to becomedistorted is responsive to an amplitude of the radar receive signalexceeding a saturation threshold of the transceiver.
 15. The method ofclaim 14, further comprising: increasing an amplitude of the radartransmit signal to increase a sensitivity of the transceiver, theincreasing of the amplitude increasing a likelihood of an amplitude ofanother radar receive signal to exceed the saturation threshold of thetransceiver.
 16. The method of claim 12, further comprising: trainingthe machine-learned module, the training of the machine-learned modulecomprising: generating sinusoidal signals to simulate non-saturatedradar receive signals; providing the sinusoidal signals as truth data tothe machine-learned module; generating non-sinusoidal signals havingdifferent clipped amplitudes to simulate probable saturated radarreceive signals having similar frequencies as the sinusoidal signals;and providing the non-sinusoidal signals as a training data sequence tothe machine-learned module.
 17. The method of claim 12, furthercomprising: training the machine-learned module, the training of themachine-learned module comprising: accepting, from a proximity sensor,measurement data associated with different distances between a radarsystem and the user during a given time period; transmitting, using theradar system, at least one other radar transmit signal during the giventime period; receiving, using the radar system, at least one other radartransmit signal associated with the at least one other radar transmitsignal during the given time period; generating truth data based on themeasurement data; generating a training data sequence based on the atleast one radar receive signal; and providing the training data sequenceand the truth data to the machine learned module.
 18. A non-transitorycomputer-readable storage media comprising computer-executableinstructions that, responsive to execution by a processor, implement: asaturation compensation module configured to: accept an input datasequence associated with a saturated radar receive signal, the saturatedradar receive signal having a clipped amplitude that distorts a motioncomponent signal associated with a gesture performed by a user, thesaturated radar receive signal including a distorted version of themotion component signal; and recover, using machine learning, the motioncomponent signal from the input data sequence to produce a predicteddata sequence based on the motion component signal, the predicted datasequence comprising a sinusoidal signal; and a gesture recognitionmodule configured to determine the gesture based on the predicted datasequence.
 19. The non-transitory computer-readable storage media ofclaim 18, wherein the saturation compensation module includes: anormalization module configured to normalize the input data sequence toproduce a normalized data sequence; and a machine-learned moduleconfigured to: accept a set of normalized samples within the normalizeddata sequence based on a temporal processing window; and recover themotion component signal from the set of normalized samples to produce aset of predicted samples associated with the predicted data sequence,the set of normalized samples and the set of predicted samples havingsimilar quantities of samples based on a size of the temporal processingwindow.
 20. The non-transitory computer-readable storage media of claim19, wherein: the saturation compensation module includes a trainingmodule configured to: generate sinusoidal signals to simulatenon-saturated radar receive signals, the sinusoidal signals representingtruth data; generate non-sinusoidal signals having different clippedamplitudes to simulate probable saturated radar receive signals havingsimilar frequencies as the sinusoidal signals, the non-sinusoidalsignals representing a training data sequence; and provide thesinusoidal signals and the non-sinusoidal signals to the machine learnedmodule; and the machine-learned module is configured to execute atraining procedure based on the sinusoidal signals and thenon-sinusoidal signals.